MiniMax-Text-01-hf

This repository is primarily for the Transformers framework. If you're using other open-source frameworks, please use the alternative repository: MiniMax-Text-01 WeChat MiniMax-Text-01 1. Introduction MiniMax-Text-01 is a powerful language model with 456 billion total parameters, of which 45.9 billion are activated per token. To better unlock the long context capabilities of the model, MiniMax-Text-01 adopts a hybrid architecture that combines Lightning Attention, Softmax Attention and Mixture-of-Experts (MoE). Leveraging advanced parallel strategies and innovative compute-communication overlap methods—such as Linear Attention Sequence Parallelism Plus (LASP+), varlen ring attention, Expert Tensor Parallel (ETP), etc., MiniMax-Text-01's training context length is extended to 1 million tokens, and it can handle a context of up to 4 million tokens during the inference. On various academic benchmarks, MiniMax-Text-01 also demonstrates the performance of a top-tier model. 2. Model Architecture The architecture of MiniMax-Text-01 is briefly described as follows: Total Parameters: 456B Activated Parameters per Token: 45.9B Number Layers: 80 Hybrid Attention: a softmax attention is positioned after every 7 lightning attention. Number of attention heads: 64 Attention head dimension: 128 Mixture of Experts: Number of experts: 32 Expert hidden dimension: 9216 Top-2 routing strategy Positional Encoding: Rotary Position Embedding (RoPE) applied to half of the attention head dimension with a base frequency of 10,000,000 Hidden Size: 6144 Vocab Size: 200,064 3. Evaluation Core Academic Benchmarks | **Tasks** | **GPT-4o (11-20)** | **Claude-3.5-Sonnet (10-22)** | **Gemini-1.5-Pro (002)** | **Gemini-2.0-Flash (exp)** | **Qwen2.5-72B-Inst.** | **DeepSeek-V3** | **Llama-3.1-405B-Inst.** | **MiniMax-Text-01** | |-------------------------------|--------------------|-------------------------------|--------------------------|----------------------------|-----------------------|----------